Overview

Dataset statistics

Number of variables20
Number of observations1947
Missing cells2219
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory319.4 KiB
Average record size in memory168.0 B

Variable types

Text2
Numeric18

Alerts

Total Agri Production is highly overall correlated with GDP(1000 USD) and 3 other fieldsHigh correlation
GDP(1000 USD) is highly overall correlated with Total Agri Production and 7 other fieldsHigh correlation
GDP per capita is highly overall correlated with GDP(1000 USD) and 6 other fieldsHigh correlation
No of frost days is highly overall correlated with Avg temperature and 1 other fieldsHigh correlation
Avg temperature is highly overall correlated with No of frost days and 2 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, both sexes (%) is highly overall correlated with GDP(1000 USD) and 6 other fieldsHigh correlation
Area is highly overall correlated with Total Agri Production and 2 other fieldsHigh correlation
Population is highly overall correlated with Total Agri Production and 3 other fieldsHigh correlation
Fertilizer Use Per Area is highly overall correlated with GDP(1000 USD) and 3 other fieldsHigh correlation
Fertilizer Use Per Capita is highly overall correlated with GDP(1000 USD) and 5 other fieldsHigh correlation
Credit to Agriculture is highly overall correlated with Total Agri Production and 4 other fieldsHigh correlation
Agriculture share of Government Expenditure is highly overall correlated with GDP per capita and 1 other fieldsHigh correlation
Water Use Efficiency is highly overall correlated with GDP per capitaHigh correlation
Gini coefficient is highly overall correlated with No of frost days and 1 other fieldsHigh correlation
Agri_Prod_Per_Capita is highly overall correlated with GDP(1000 USD) and 3 other fieldsHigh correlation
Credit to Agriculture has 1341 (68.9%) missing valuesMissing
FDI inflows to Agriculture has 498 (25.6%) missing valuesMissing
Agriculture share of Government Expenditure has 113 (5.8%) missing valuesMissing
Water Use Efficiency has 57 (2.9%) missing valuesMissing
Gini coefficient has 210 (10.8%) missing valuesMissing
Total Agri Production has unique valuesUnique
GDP(1000 USD) has unique valuesUnique
GDP per capita has unique valuesUnique
Population has unique valuesUnique
Agri_Prod_Per_Capita has unique valuesUnique
No of frost days has 740 (38.0%) zerosZeros
Water Use Efficiency has 66 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-13 21:15:02.672065
Analysis finished2023-12-13 21:15:37.638126
Duration34.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct126
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:37.795037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length24
Median length18
Mean length7.5711351
Min length4

Characters and Unicode

Total characters14741
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowAlbania
2nd rowAlbania
3rd rowAlbania
4th rowAlbania
5th rowAlbania
ValueCountFrequency (%)
indonesia 28
 
1.3%
france 28
 
1.3%
finland 28
 
1.3%
sweden 28
 
1.3%
spain 28
 
1.3%
norway 28
 
1.3%
italy 28
 
1.3%
cyprus 27
 
1.3%
switzerland 27
 
1.3%
slovenia 27
 
1.3%
Other values (131) 1856
87.0%
2023-12-13T13:15:38.078511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2483
16.8%
i 1324
 
9.0%
n 1176
 
8.0%
e 967
 
6.6%
r 942
 
6.4%
o 733
 
5.0%
l 712
 
4.8%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.2%
Other values (40) 4847
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12422
84.3%
Uppercase Letter 2129
 
14.4%
Space Separator 186
 
1.3%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2483
20.0%
i 1324
10.7%
n 1176
9.5%
e 967
 
7.8%
r 942
 
7.6%
o 733
 
5.9%
l 712
 
5.7%
u 585
 
4.7%
t 495
 
4.0%
d 477
 
3.8%
Other values (16) 2528
20.4%
Uppercase Letter
ValueCountFrequency (%)
M 234
11.0%
S 206
 
9.7%
B 194
 
9.1%
C 185
 
8.7%
I 154
 
7.2%
A 143
 
6.7%
P 136
 
6.4%
N 119
 
5.6%
G 106
 
5.0%
F 103
 
4.8%
Other values (12) 549
25.8%
Space Separator
ValueCountFrequency (%)
186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14551
98.7%
Common 190
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2483
17.1%
i 1324
 
9.1%
n 1176
 
8.1%
e 967
 
6.6%
r 942
 
6.5%
o 733
 
5.0%
l 712
 
4.9%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.3%
Other values (38) 4657
32.0%
Common
ValueCountFrequency (%)
186
97.9%
- 4
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2483
16.8%
i 1324
 
9.0%
n 1176
 
8.0%
e 967
 
6.6%
r 942
 
6.4%
o 733
 
5.0%
l 712
 
4.8%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.2%
Other values (40) 4847
32.9%

Year
Real number (ℝ)

Distinct29
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5172
Minimum1991
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:38.188517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1992
Q11999
median2006
Q32012
95-th percentile2017
Maximum2019
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9921652
Coefficient of variation (CV)0.0039850893
Kurtosis-1.106318
Mean2005.5172
Median Absolute Deviation (MAD)7
Skewness-0.15736562
Sum3904742
Variance63.874704
MonotonicityNot monotonic
2023-12-13T13:15:38.267117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2011 81
 
4.2%
2010 80
 
4.1%
2005 79
 
4.1%
2017 78
 
4.0%
2015 77
 
4.0%
2012 77
 
4.0%
2009 77
 
4.0%
2014 76
 
3.9%
2008 76
 
3.9%
2006 75
 
3.9%
Other values (19) 1171
60.1%
ValueCountFrequency (%)
1991 59
3.0%
1992 54
2.8%
1993 61
3.1%
1994 62
3.2%
1995 62
3.2%
1996 61
3.1%
1997 40
2.1%
1998 47
2.4%
1999 68
3.5%
2000 64
3.3%
ValueCountFrequency (%)
2019 23
 
1.2%
2018 68
3.5%
2017 78
4.0%
2016 75
3.9%
2015 77
4.0%
2014 76
3.9%
2013 72
3.7%
2012 77
4.0%
2011 81
4.2%
2010 80
4.1%

Total Agri Production
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44875692
Minimum296
Maximum3.5353873 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:38.361325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum296
5-th percentile184524.8
Q11712568
median7002967
Q321676264
95-th percentile1.078384 × 108
Maximum3.5353873 × 109
Range3.535387 × 109
Interquartile range (IQR)19963696

Descriptive statistics

Standard deviation2.2379089 × 108
Coefficient of variation (CV)4.9869067
Kurtosis129.77568
Mean44875692
Median Absolute Deviation (MAD)5982424
Skewness10.838162
Sum8.7372971 × 1010
Variance5.0082361 × 1016
MonotonicityNot monotonic
2023-12-13T13:15:38.471131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1885819 1
 
0.1%
5977448 1
 
0.1%
5998273 1
 
0.1%
6076422 1
 
0.1%
5401121 1
 
0.1%
5561801 1
 
0.1%
5029095 1
 
0.1%
4729117 1
 
0.1%
4501672 1
 
0.1%
4417239 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
296 1
0.1%
361 1
0.1%
12531 1
0.1%
15465 1
0.1%
16208 1
0.1%
16648 1
0.1%
18165 1
0.1%
18564 1
0.1%
20884 1
0.1%
21415 1
0.1%
ValueCountFrequency (%)
3535387313 1
0.1%
2925750697 1
0.1%
2841977977 1
0.1%
2840032663 1
0.1%
2835023231 1
0.1%
2775716224 1
0.1%
2766587295 1
0.1%
2662942827 1
0.1%
2619834639 1
0.1%
2032418918 1
0.1%

GDP(1000 USD)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0685466 × 108
Minimum586795.63
Maximum1.4698222 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:38.581924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum586795.63
5-th percentile1818154.8
Q19697771.5
median46658765
Q32.3972704 × 108
95-th percentile1.4710856 × 109
Maximum1.4698222 × 1010
Range1.4697635 × 1010
Interquartile range (IQR)2.3002927 × 108

Descriptive statistics

Standard deviation9.4354267 × 108
Coefficient of variation (CV)3.0748846
Kurtosis107.61935
Mean3.0685466 × 108
Median Absolute Deviation (MAD)43341446
Skewness9.0549583
Sum5.9744603 × 1011
Variance8.9027277 × 1017
MonotonicityNot monotonic
2023-12-13T13:15:38.675636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1700439.125 1
 
0.1%
8542083.076 1
 
0.1%
6640050.345 1
 
0.1%
5979106.917 1
 
0.1%
5890906.18 1
 
0.1%
5730380.115 1
 
0.1%
4713497.173 1
 
0.1%
4534418.924 1
 
0.1%
4330701.346 1
 
0.1%
3787172.382 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
586795.628 1
0.1%
591839.46 1
0.1%
628101.556 1
0.1%
639723.72 1
0.1%
654572.445 1
0.1%
662345.748 1
0.1%
679885.548 1
0.1%
706370.812 1
0.1%
726881.639 1
0.1%
729321.669 1
0.1%
ValueCountFrequency (%)
1.469822224 × 10101
0.1%
1.431192091 × 10101
0.1%
1.270220398 × 10101
0.1%
1.159924424 × 10101
0.1%
1.141600351 × 10101
0.1%
1.08219859 × 10101
0.1%
9897700345 1
0.1%
8838004954 1
0.1%
7836902033 1
0.1%
6344068421 1
0.1%

GDP per capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13792.068
Minimum94.466448
Maximum119940.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:38.785691image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum94.466448
5-th percentile331.62174
Q11541.1599
median5359.2391
Q319019.03
95-th percentile52633.895
Maximum119940.37
Range119845.9
Interquartile range (IQR)17477.87

Descriptive statistics

Standard deviation18754.519
Coefficient of variation (CV)1.3598047
Kurtosis5.5135113
Mean13792.068
Median Absolute Deviation (MAD)4757.7823
Skewness2.1882247
Sum26853157
Variance3.51732 × 108
MonotonicityNot monotonic
2023-12-13T13:15:38.895608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515.173587 1
 
0.1%
422.642863 1
 
0.1%
258.539662 1
 
0.1%
236.028142 1
 
0.1%
236.051027 1
 
0.1%
233.326416 1
 
0.1%
206.877791 1
 
0.1%
203.286386 1
 
0.1%
198.703869 1
 
0.1%
178.074409 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
94.466448 1
0.1%
95.482892 1
0.1%
102.439454 1
0.1%
108.213069 1
0.1%
112.611423 1
0.1%
115.381036 1
0.1%
115.54302 1
0.1%
119.796791 1
0.1%
128.505986 1
0.1%
131.94554 1
0.1%
ValueCountFrequency (%)
119940.3682 1
0.1%
116793.6197 1
0.1%
112667.7211 1
0.1%
110752.3473 1
0.1%
110203.0082 1
0.1%
106581.887 1
0.1%
105455.8957 1
0.1%
102892.2834 1
0.1%
101514.3718 1
0.1%
100590.9564 1
0.1%
Distinct126
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:39.084464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5841
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowALB
2nd rowALB
3rd rowALB
4th rowALB
5th rowALB
ValueCountFrequency (%)
idn 28
 
1.4%
swe 28
 
1.4%
nor 28
 
1.4%
esp 28
 
1.4%
ita 28
 
1.4%
fin 28
 
1.4%
reu 28
 
1.4%
alb 27
 
1.4%
mex 27
 
1.4%
prt 27
 
1.4%
Other values (116) 1670
85.8%
2023-12-13T13:15:39.335850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5841
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5841
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

No of frost days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct814
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3223986
Minimum0
Maximum20.21
Zeros740
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:39.430057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.89
Q37.805
95-th percentile15.945
Maximum20.21
Range20.21
Interquartile range (IQR)7.805

Descriptive statistics

Standard deviation5.5455188
Coefficient of variation (CV)1.2829726
Kurtosis0.086561791
Mean4.3223986
Median Absolute Deviation (MAD)0.89
Skewness1.1123251
Sum8415.71
Variance30.752778
MonotonicityNot monotonic
2023-12-13T13:15:39.541667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 740
38.0%
0.01 22
 
1.1%
0.02 14
 
0.7%
0.07 12
 
0.6%
0.04 10
 
0.5%
0.05 9
 
0.5%
0.13 7
 
0.4%
0.12 6
 
0.3%
0.21 5
 
0.3%
0.11 5
 
0.3%
Other values (804) 1117
57.4%
ValueCountFrequency (%)
0 740
38.0%
0.01 22
 
1.1%
0.02 14
 
0.7%
0.03 4
 
0.2%
0.04 10
 
0.5%
0.05 9
 
0.5%
0.06 5
 
0.3%
0.07 12
 
0.6%
0.08 5
 
0.3%
0.09 2
 
0.1%
ValueCountFrequency (%)
20.21 2
0.1%
20.04 1
0.1%
19.79 1
0.1%
19.77 1
0.1%
19.76 1
0.1%
19.74 1
0.1%
19.66 2
0.1%
19.58 1
0.1%
19.54 1
0.1%
19.48 1
0.1%

Precipitation
Real number (ℝ)

Distinct1844
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.388665
Minimum0.85
Maximum335.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:39.650051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile9.578
Q152.85
median75.34
Q3118.42
95-th percentile248.644
Maximum335.77
Range334.92
Interquartile range (IQR)65.57

Descriptive statistics

Standard deviation64.82247
Coefficient of variation (CV)0.70162795
Kurtosis1.7183209
Mean92.388665
Median Absolute Deviation (MAD)32.05
Skewness1.3192265
Sum179880.73
Variance4201.9526
MonotonicityNot monotonic
2023-12-13T13:15:39.744363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.39 3
 
0.2%
66.85 3
 
0.2%
49.89 2
 
0.1%
151.24 2
 
0.1%
66.57 2
 
0.1%
44.36 2
 
0.1%
46.35 2
 
0.1%
126.71 2
 
0.1%
158.55 2
 
0.1%
62.18 2
 
0.1%
Other values (1834) 1925
98.9%
ValueCountFrequency (%)
0.85 1
0.1%
1.47 1
0.1%
1.78 1
0.1%
1.93 1
0.1%
2.21 1
0.1%
2.34 1
0.1%
2.93 1
0.1%
2.94 1
0.1%
3.12 1
0.1%
3.3 1
0.1%
ValueCountFrequency (%)
335.77 1
0.1%
325.68 1
0.1%
318.52 1
0.1%
313.86 1
0.1%
309.09 1
0.1%
308.63 1
0.1%
306.84 1
0.1%
306.06 1
0.1%
305.58 1
0.1%
303.97 1
0.1%

Avg temperature
Real number (ℝ)

HIGH CORRELATION 

Distinct1359
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.400077
Minimum-4.96
Maximum29.78
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)2.7%
Memory size30.4 KiB
2023-12-13T13:15:39.854238image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.96
5-th percentile1.97
Q19.34
median18.34
Q324.615
95-th percentile27.607
Maximum29.78
Range34.74
Interquartile range (IQR)15.275

Descriptive statistics

Standard deviation8.6651123
Coefficient of variation (CV)0.52835802
Kurtosis-1.0314779
Mean16.400077
Median Absolute Deviation (MAD)7.42
Skewness-0.3306468
Sum31930.95
Variance75.084171
MonotonicityNot monotonic
2023-12-13T13:15:39.948451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.37 7
 
0.4%
25.17 6
 
0.3%
23.98 6
 
0.3%
26.5 5
 
0.3%
20.81 5
 
0.3%
25.95 5
 
0.3%
23.62 5
 
0.3%
12.55 4
 
0.2%
25.82 4
 
0.2%
7.42 4
 
0.2%
Other values (1349) 1896
97.4%
ValueCountFrequency (%)
-4.96 1
0.1%
-4.88 1
0.1%
-4.79 1
0.1%
-4.4 1
0.1%
-4.38 1
0.1%
-4.36 1
0.1%
-4.35 1
0.1%
-4.34 1
0.1%
-4.33 1
0.1%
-4.26 1
0.1%
ValueCountFrequency (%)
29.78 1
0.1%
29.29 2
0.1%
29.14 1
0.1%
29.11 1
0.1%
29.09 1
0.1%
29.08 1
0.1%
29.06 1
0.1%
29.02 1
0.1%
29 1
0.1%
28.99 1
0.1%
Distinct1946
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.874749
Minimum13.86884
Maximum133.05758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:40.058287image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.86884
5-th percentile42.561947
Q167.29152
median77.84823
Q389.868985
95-th percentile101.82071
Maximum133.05758
Range119.18874
Interquartile range (IQR)22.577465

Descriptive statistics

Standard deviation18.34336
Coefficient of variation (CV)0.2386136
Kurtosis0.7030922
Mean76.874749
Median Absolute Deviation (MAD)11.35819
Skewness-0.61375974
Sum149675.14
Variance336.47887
MonotonicityNot monotonic
2023-12-13T13:15:40.153017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.28946 2
 
0.1%
61.45394 1
 
0.1%
59.4382 1
 
0.1%
59.69782 1
 
0.1%
55.74447 1
 
0.1%
56.83317 1
 
0.1%
60.95266 1
 
0.1%
58.42994 1
 
0.1%
58.16263 1
 
0.1%
58.05668 1
 
0.1%
Other values (1936) 1936
99.4%
ValueCountFrequency (%)
13.86884 1
0.1%
14.54958 1
0.1%
14.7984 1
0.1%
15.68368 1
0.1%
16.44042 1
0.1%
16.733 1
0.1%
17.16467 1
0.1%
17.20653 1
0.1%
17.80632 1
0.1%
18.42457 1
0.1%
ValueCountFrequency (%)
133.05758 1
0.1%
131.17207 1
0.1%
126.40767 1
0.1%
119.79754 1
0.1%
118.66204 1
0.1%
118.17177 1
0.1%
118.03144 1
0.1%
117.94248 1
0.1%
117.60128 1
0.1%
117.58541 1
0.1%

Area
Real number (ℝ)

HIGH CORRELATION 

Distinct1692
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30374.974
Minimum0.66
Maximum529038.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:40.262854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile66
Q11579.6553
median4995
Q327177.5
95-th percentile173642.61
Maximum529038.6
Range529037.94
Interquartile range (IQR)25597.845

Descriptive statistics

Standard deviation73578.445
Coefficient of variation (CV)2.4223377
Kurtosis25.411729
Mean30374.974
Median Absolute Deviation (MAD)4810
Skewness4.6884948
Sum59140075
Variance5.4137875 × 109
MonotonicityNot monotonic
2023-12-13T13:15:40.372812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.4 11
 
0.6%
10 10
 
0.5%
215494 10
 
0.5%
13 8
 
0.4%
40895 8
 
0.4%
1042 8
 
0.4%
1.55 7
 
0.4%
9 7
 
0.4%
38820 7
 
0.4%
4121 6
 
0.3%
Other values (1682) 1865
95.8%
ValueCountFrequency (%)
0.66 3
0.2%
1.5 2
 
0.1%
1.55 7
0.4%
2.4 1
 
0.1%
2.9 1
 
0.1%
3 1
 
0.1%
8 4
0.2%
8.11 1
 
0.1%
8.9 1
 
0.1%
9 7
0.4%
ValueCountFrequency (%)
529038.6 1
0.1%
528451.7556 1
0.1%
528280.8 1
0.1%
527861.9111 1
0.1%
527526.5 1
0.1%
527272.0667 1
0.1%
526768.8 1
0.1%
526013.5 1
0.1%
525260.8 1
0.1%
524505.5 1
0.1%

Population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50737959
Minimum84534
Maximum1.4538015 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:40.467031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum84534
5-th percentile407482.7
Q13322607
median9259362
Q328206781
95-th percentile1.4879806 × 108
Maximum1.4538015 × 109
Range1.453717 × 109
Interquartile range (IQR)24884174

Descriptive statistics

Standard deviation1.8515789 × 108
Coefficient of variation (CV)3.6492971
Kurtosis39.947938
Mean50737959
Median Absolute Deviation (MAD)7296497
Skewness6.3011607
Sum9.8786806 × 1010
Variance3.4283443 × 1016
MonotonicityNot monotonic
2023-12-13T13:15:40.576813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3300711 1
 
0.1%
20211114 1
 
0.1%
25682908 1
 
0.1%
25332178 1
 
0.1%
24956071 1
 
0.1%
24559500 1
 
0.1%
22783969 1
 
0.1%
22305571 1
 
0.1%
21794751 1
 
0.1%
21267359 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
84534 1
0.1%
85695 1
0.1%
86729 1
0.1%
87674 1
0.1%
89644 1
0.1%
91030 1
0.1%
92409 1
0.1%
93827 1
0.1%
95309 1
0.1%
96714 1
0.1%
ValueCountFrequency (%)
1453801543 1
0.1%
1448928199 1
0.1%
1442041109 1
0.1%
1433546767 1
0.1%
1425242661 1
0.1%
1416568531 1
0.1%
1407320843 1
0.1%
1397611702 1
0.1%
1387984919 1
0.1%
1383112050 1
0.1%

Fertilizer Use Per Area
Real number (ℝ)

HIGH CORRELATION 

Distinct1867
Distinct (%)95.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.12509
Minimum0
Maximum450.24
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:40.688373image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.606
Q125.89
median80.87
Q3134.49
95-th percentile268.901
Maximum450.24
Range450.24
Interquartile range (IQR)108.6

Descriptive statistics

Standard deviation84.718563
Coefficient of variation (CV)0.88133664
Kurtosis1.3498845
Mean96.12509
Median Absolute Deviation (MAD)54.44
Skewness1.2050949
Sum187155.55
Variance7177.235
MonotonicityNot monotonic
2023-12-13T13:15:40.796353image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.9 6
 
0.3%
99.99 4
 
0.2%
0.27 4
 
0.2%
0.36 3
 
0.2%
118.32 3
 
0.2%
0.38 3
 
0.2%
187.5 3
 
0.2%
36.44 2
 
0.1%
116.66 2
 
0.1%
100.62 2
 
0.1%
Other values (1857) 1915
98.4%
ValueCountFrequency (%)
0 1
0.1%
0.01 2
0.1%
0.05 1
0.1%
0.07 2
0.1%
0.1 1
0.1%
0.11 1
0.1%
0.12 1
0.1%
0.12 1
0.1%
0.15 1
0.1%
0.24 1
0.1%
ValueCountFrequency (%)
450.24 1
0.1%
423.43 1
0.1%
421.95 1
0.1%
419.96 1
0.1%
414.15 1
0.1%
411.26 1
0.1%
405.13 1
0.1%
403.81 1
0.1%
403.74 1
0.1%
400 2
0.1%

Fertilizer Use Per Capita
Real number (ℝ)

HIGH CORRELATION 

Distinct1682
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.020252
Minimum0
Maximum211.27
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:40.890680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.573
Q14.345
median16.26
Q335.88
95-th percentile85.36
Maximum211.27
Range211.27
Interquartile range (IQR)31.535

Descriptive statistics

Standard deviation30.912043
Coefficient of variation (CV)1.1879994
Kurtosis8.4694922
Mean26.020252
Median Absolute Deviation (MAD)13.6
Skewness2.513806
Sum50661.43
Variance955.55439
MonotonicityNot monotonic
2023-12-13T13:15:41.001118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 6
 
0.3%
2.21 5
 
0.3%
0.03 4
 
0.2%
1.8 4
 
0.2%
0.28 4
 
0.2%
0.99 4
 
0.2%
0.53 4
 
0.2%
0.33 4
 
0.2%
0.15 4
 
0.2%
1.75 4
 
0.2%
Other values (1672) 1904
97.8%
ValueCountFrequency (%)
0 2
 
0.1%
0.01 6
0.3%
0.02 1
 
0.1%
0.03 4
0.2%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.06 1
 
0.1%
0.08 1
 
0.1%
0.09 1
 
0.1%
0.11 2
 
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
208.97 1
0.1%
201.94 1
0.1%
193.55 1
0.1%
190.35 1
0.1%
189.74 1
0.1%
189.33 1
0.1%
189.09 1
0.1%
188.35 1
0.1%
187.09 1
0.1%

Credit to Agriculture
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct402
Distinct (%)66.3%
Missing1341
Missing (%)68.9%
Infinite0
Infinite (%)0.0%
Mean4.0088955 × 109
Minimum22750
Maximum1.6745882 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:41.094895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22750
5-th percentile1908322.5
Q120880190
median1.5218554 × 108
Q32.3454454 × 109
95-th percentile1.3625657 × 1010
Maximum1.6745882 × 1011
Range1.674588 × 1011
Interquartile range (IQR)2.3245652 × 109

Descriptive statistics

Standard deviation1.5685228 × 1010
Coefficient of variation (CV)3.9126058
Kurtosis57.915123
Mean4.0088955 × 109
Median Absolute Deviation (MAD)1.4895162 × 108
Skewness7.2791102
Sum2.4293907 × 1012
Variance2.4602637 × 1020
MonotonicityNot monotonic
2023-12-13T13:15:41.204907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4780220 15
 
0.8%
3380230821 13
 
0.7%
626275204 13
 
0.7%
145252153 13
 
0.7%
2322587105 12
 
0.6%
299843630 12
 
0.6%
2932418 10
 
0.5%
3579285995 10
 
0.5%
2315782599 10
 
0.5%
1565934 9
 
0.5%
Other values (392) 489
 
25.1%
(Missing) 1341
68.9%
ValueCountFrequency (%)
22750 1
 
0.1%
22949 1
 
0.1%
110922 1
 
0.1%
193377 1
 
0.1%
548755 1
 
0.1%
676187 1
 
0.1%
820313 2
 
0.1%
872220 6
0.3%
968685 2
 
0.1%
1251219 1
 
0.1%
ValueCountFrequency (%)
1.67458821 × 10111
0.1%
1.52580569 × 10111
0.1%
1.299489373 × 10111
0.1%
1.264921285 × 10111
0.1%
1.183343021 × 10111
0.1%
1.153926557 × 10111
0.1%
9.878236343 × 10101
0.1%
8.770622482 × 10101
0.1%
8.535623139 × 10101
0.1%
6.393300178 × 10101
0.1%

FDI inflows to Agriculture
Real number (ℝ)

MISSING 

Distinct1006
Distinct (%)69.4%
Missing498
Missing (%)25.6%
Infinite0
Infinite (%)0.0%
Mean43444558
Minimum-1.2093895 × 109
Maximum4.93221 × 109
Zeros0
Zeros (%)0.0%
Negative333
Negative (%)17.1%
Memory size30.4 KiB
2023-12-13T13:15:41.314628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2093895 × 109
5-th percentile-22478921
Q112
median3693038
Q322515214
95-th percentile1.9935133 × 108
Maximum4.93221 × 109
Range6.1415995 × 109
Interquartile range (IQR)22515202

Descriptive statistics

Standard deviation2.5953516 × 108
Coefficient of variation (CV)5.9739394
Kurtosis178.2757
Mean43444558
Median Absolute Deviation (MAD)5596402
Skewness11.670494
Sum6.2951165 × 1010
Variance6.7358499 × 1016
MonotonicityNot monotonic
2023-12-13T13:15:41.424456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 30
 
1.5%
-333505 26
 
1.3%
-660000 22
 
1.1%
2000000 20
 
1.0%
59095 18
 
0.9%
-585930 17
 
0.9%
-108514000 13
 
0.7%
-108125 12
 
0.6%
15330857 12
 
0.6%
6870265 12
 
0.6%
Other values (996) 1267
65.1%
(Missing) 498
 
25.6%
ValueCountFrequency (%)
-1209389531 1
0.1%
-972491668 1
0.1%
-661112651 1
0.1%
-425491573 2
0.1%
-419391308 1
0.1%
-400034541 1
0.1%
-382105017 1
0.1%
-366852658 1
0.1%
-334451348 1
0.1%
-273647000 1
0.1%
ValueCountFrequency (%)
4932210000 1
0.1%
4157780000 1
0.1%
3632120000 1
0.1%
3141160000 1
0.1%
2345480000 1
0.1%
2016398000 1
0.1%
1967640000 1
0.1%
1346195657 1
0.1%
1282000000 1
0.1%
1235222379 1
0.1%

Agriculture share of Government Expenditure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct537
Distinct (%)29.3%
Missing113
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean2.8423773
Minimum0.09
Maximum24.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:41.534805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.33
Q11.21
median2.15
Q33.57
95-th percentile7.4805
Maximum24.49
Range24.4
Interquartile range (IQR)2.36

Descriptive statistics

Standard deviation2.7393325
Coefficient of variation (CV)0.96374694
Kurtosis13.48451
Mean2.8423773
Median Absolute Deviation (MAD)1.125
Skewness3.0503585
Sum5212.92
Variance7.5039423
MonotonicityNot monotonic
2023-12-13T13:15:41.633597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69 26
 
1.3%
0.84 20
 
1.0%
3.41 20
 
1.0%
0.93 17
 
0.9%
1.63 16
 
0.8%
1.3 16
 
0.8%
1.89 15
 
0.8%
1.68 15
 
0.8%
4.69 14
 
0.7%
3.23 14
 
0.7%
Other values (527) 1661
85.3%
(Missing) 113
 
5.8%
ValueCountFrequency (%)
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 2
 
0.1%
0.12 3
 
0.2%
0.15 1
 
0.1%
0.16 3
 
0.2%
0.18 6
0.3%
0.19 12
0.6%
0.2 1
 
0.1%
0.21 1
 
0.1%
ValueCountFrequency (%)
24.49 1
 
0.1%
19.85 10
0.5%
17.75 1
 
0.1%
17.11 1
 
0.1%
16.37 1
 
0.1%
16.3 1
 
0.1%
16.16 1
 
0.1%
15.96 1
 
0.1%
15.93 1
 
0.1%
15.61 1
 
0.1%

Water Use Efficiency
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct273
Distinct (%)14.4%
Missing57
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean0.89304762
Minimum0
Maximum13.01
Zeros66
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:41.738835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.09
median0.26
Q30.71
95-th percentile4.17
Maximum13.01
Range13.01
Interquartile range (IQR)0.62

Descriptive statistics

Standard deviation1.8556668
Coefficient of variation (CV)2.0779035
Kurtosis21.438722
Mean0.89304762
Median Absolute Deviation (MAD)0.21
Skewness4.2658827
Sum1687.86
Variance3.4434993
MonotonicityNot monotonic
2023-12-13T13:15:41.848509image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03 74
 
3.8%
0.05 69
 
3.5%
0.18 67
 
3.4%
0 66
 
3.4%
0.04 57
 
2.9%
0.02 50
 
2.6%
0.07 39
 
2.0%
0.15 38
 
2.0%
0.06 37
 
1.9%
0.1 35
 
1.8%
Other values (263) 1358
69.7%
(Missing) 57
 
2.9%
ValueCountFrequency (%)
0 66
3.4%
0.01 32
1.6%
0.02 50
2.6%
0.03 74
3.8%
0.04 57
2.9%
0.05 69
3.5%
0.06 37
1.9%
0.07 39
2.0%
0.08 30
1.5%
0.09 35
1.8%
ValueCountFrequency (%)
13.01 19
1.0%
10.22 14
0.7%
7.51 1
 
0.1%
7.27 1
 
0.1%
7.06 1
 
0.1%
6.95 1
 
0.1%
6.87 1
 
0.1%
6.74 1
 
0.1%
6.64 1
 
0.1%
6.63 1
 
0.1%

Gini coefficient
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1343
Distinct (%)77.3%
Missing210
Missing (%)10.8%
Infinite0
Infinite (%)0.0%
Mean0.37079691
Minimum0.22879762
Maximum0.6331876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:41.943484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22879762
5-th percentile0.26626759
Q10.30899726
median0.35222571
Q30.42179673
95-th percentile0.53712081
Maximum0.6331876
Range0.40438998
Interquartile range (IQR)0.11279948

Descriptive statistics

Standard deviation0.084111108
Coefficient of variation (CV)0.22683875
Kurtosis0.43933617
Mean0.37079691
Median Absolute Deviation (MAD)0.051831986
Skewness0.8953943
Sum644.07423
Variance0.0070746784
MonotonicityNot monotonic
2023-12-13T13:15:42.037684image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3303795578 18
 
0.9%
0.2801997614 16
 
0.8%
0.3008940076 13
 
0.7%
0.2683603697 12
 
0.6%
0.3877535169 12
 
0.6%
0.4594159019 12
 
0.6%
0.353803358 10
 
0.5%
0.4221257523 10
 
0.5%
0.3565342212 10
 
0.5%
0.4071830247 10
 
0.5%
Other values (1333) 1614
82.9%
(Missing) 210
 
10.8%
ValueCountFrequency (%)
0.2287976171 1
0.1%
0.2295589642 1
0.1%
0.2302460553 1
0.1%
0.2315850224 1
0.1%
0.2316944935 1
0.1%
0.2331429317 1
0.1%
0.2336110806 1
0.1%
0.2345913699 1
0.1%
0.2353520108 1
0.1%
0.2356371389 1
0.1%
ValueCountFrequency (%)
0.6331875979 7
0.4%
0.6324036513 6
0.3%
0.6302607273 9
0.5%
0.613307461 3
 
0.2%
0.6079065038 8
0.4%
0.5873960008 1
 
0.1%
0.5868289301 1
 
0.1%
0.5821961738 1
 
0.1%
0.5815903937 2
 
0.1%
0.5811751924 1
 
0.1%

Agri_Prod_Per_Capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean875.38899
Minimum1.0127137
Maximum4690.0415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-12-13T13:15:42.147968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.0127137
5-th percentile139.98234
Q1405.85494
median728.68936
Q31200.1201
95-th percentile2146.9748
Maximum4690.0415
Range4689.0288
Interquartile range (IQR)794.26512

Descriptive statistics

Standard deviation635.68583
Coefficient of variation (CV)0.72617526
Kurtosis2.3406303
Mean875.38899
Median Absolute Deviation (MAD)358.42771
Skewness1.3682678
Sum1704382.4
Variance404096.47
MonotonicityNot monotonic
2023-12-13T13:15:42.242178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
571.3372058 1
 
0.1%
295.750546 1
 
0.1%
233.5511617 1
 
0.1%
239.8697025 1
 
0.1%
216.4251336 1
 
0.1%
226.4623058 1
 
0.1%
220.7295401 1
 
0.1%
212.0150612 1
 
0.1%
206.5484483 1
 
0.1%
207.7004014 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
1.012713662 1
0.1%
1.214564002 1
0.1%
8.984165916 1
0.1%
12.1046208 1
0.1%
13.97824933 1
0.1%
14.88768916 1
0.1%
16.49928667 1
0.1%
17.56364592 1
0.1%
20.93958286 1
0.1%
21.70491145 1
0.1%
ValueCountFrequency (%)
4690.041525 1
0.1%
3650.151094 1
0.1%
3593.730364 1
0.1%
3587.088299 1
0.1%
3549.855931 1
0.1%
3435.208578 1
0.1%
3413.282865 1
0.1%
3380.458142 1
0.1%
3325.858827 1
0.1%
3239.049977 1
0.1%

Interactions

2023-12-13T13:15:35.593198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:03.761406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.916437image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.942889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.833705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.817570image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.691721image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.681051image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.543907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.451705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.399698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.227727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.005241image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.754012image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.820819image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.381196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.990527image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.561583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.689369image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:03.891777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.021196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.044053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.927325image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.920147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.785932image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.780543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.650442image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.548656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.498010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.331633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.097931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.855334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.905049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.473265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.074664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.654377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.780910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.039097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.134767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.159815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.049484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.026754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.902727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.890084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.755791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.654324image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.595963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.432790image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.202590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.942949image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.987430image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.559128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.143822image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.759298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.869489image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.217217image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.255308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.268933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.156754image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.131919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.007010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.993741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.900529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.754093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.698025image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.532316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.304507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.040906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.073700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.653266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.246717image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.845574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.948455image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.385394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.365230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.368313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.257847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.231495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.110098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.090847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.004528image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.851351image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.800685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.630907image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.400848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.134593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.165160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.743650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.337337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.940098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.038305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.510535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.616264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.487117image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.514963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.336951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.214601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.197350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.110602image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.951811image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.908868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.731155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.501212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.241743image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.259423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.831072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.430466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.041334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.123779image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.628438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.720279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.590619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.617140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.453550image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.316697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.301258image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.216910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.048275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.013752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.826434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.601985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.351876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.344988image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.919035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.520604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.130321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.205261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.739619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.826481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.695763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.721979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.557196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.417494image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.403268image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.322623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.144019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.114027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.924159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.700800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.508115image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.433252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.003997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.612406image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.219898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.291088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.845878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:06.931290image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.795153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.818389image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.651622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.523057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.512157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.425655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.243448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.211578image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.020905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.794612image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:27.620453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.523212image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.089959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.697319image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.311091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.376371image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:04.945663image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.025420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.893625image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.911124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.760863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.627992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.608617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.529562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.332525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.312317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.125961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.884306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.001462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.604705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.172831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.772163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.401299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.455004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.054860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.132994image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:08.999109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:10.999300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.864784image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.731516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.710411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.633417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.433078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.428263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.227203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:25.981763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.104774image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.699294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.260556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.873101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.496236image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.535738image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.170370image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.230418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.095848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.102876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:12.970563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:14.823453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.805449image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.733828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.523052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.530276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.315678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.076621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.191635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.778572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.354752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:32.951586image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.581720image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.614243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.257291image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.328727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.204047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.193931image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.061141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.079193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:16.896204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.829097image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.612150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.624601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.406002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.168802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.273820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.863488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.436542image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.035854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.664143image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.708339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.384300image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.436350image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.314195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.305845image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.173690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.190501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.000378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:18.940320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:20.935991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.728219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.514647image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.276666image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.377475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:29.948434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.533520image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.125487image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.754127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.798618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.496144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.536952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.408654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.415848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.277276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.293879image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.094503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.042755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.038952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.830224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.611567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.375443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.457713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.034024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.622145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.213890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.834914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.880750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.592776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.627861image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.519992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.516662image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.383002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.382330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.221053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.145411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.124653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:22.924480image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.711786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.474272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.554264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.120424image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.712796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.299213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:34.930145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:36.959814image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.703828image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.731561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.628628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.613540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.487451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.479222image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.335096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.246471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.211280image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.019114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.806855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.569589image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.642399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.210885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.801502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.387309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.023963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:37.052198image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:05.819918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:07.843109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:09.743634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:11.718746image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:13.597516image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:15.584270image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:17.448565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:19.359805image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:21.311997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:23.133255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:24.911952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:26.670636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:28.730853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:30.307525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:31.908722image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:33.479639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-12-13T13:15:35.118959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-12-13T13:15:42.363502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YearTotal Agri ProductionGDP(1000 USD)GDP per capitaNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)AreaPopulationFertilizer Use Per AreaFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
Year1.0000.1630.2350.266-0.0570.0460.0860.333-0.0600.0480.1170.0080.0970.156-0.1600.066-0.0180.307
Total Agri Production0.1631.0000.7950.0740.1220.034-0.0910.1740.7200.8990.1790.3310.7630.359-0.195-0.0150.0290.460
GDP(1000 USD)0.2350.7951.0000.5820.281-0.023-0.2660.5290.4290.6300.5230.5450.8130.225-0.4640.271-0.1790.522
GDP per capita0.2660.0740.5821.0000.315-0.056-0.3900.804-0.303-0.2150.6560.5740.311-0.089-0.6000.543-0.4170.606
No of frost days-0.0570.1220.2810.3151.000-0.201-0.9290.4520.047-0.0290.1740.4370.375-0.058-0.1860.078-0.5610.314
Precipitation0.0460.034-0.023-0.056-0.2011.0000.114-0.022-0.1260.0230.1490.0710.1660.0430.0490.1350.2770.023
Avg temperature0.086-0.091-0.266-0.390-0.9290.1141.000-0.5240.0450.098-0.252-0.499-0.4160.1130.253-0.1100.588-0.401
Gross enrolment ratio, primary to tertiary, both sexes (%)0.3330.1740.5290.8040.452-0.022-0.5241.000-0.156-0.1050.5140.6040.4230.002-0.5620.306-0.4060.622
Area-0.0600.7200.429-0.3030.047-0.1260.045-0.1561.0000.813-0.2400.0650.5230.3790.100-0.3610.2480.011
Population0.0480.8990.630-0.215-0.0290.0230.098-0.1050.8131.000-0.0430.0690.6130.3550.004-0.1640.1940.073
Fertilizer Use Per Area0.1170.1790.5230.6560.1740.149-0.2520.514-0.240-0.0431.0000.7120.494-0.080-0.4030.494-0.2680.462
Fertilizer Use Per Capita0.0080.3310.5450.5740.4370.071-0.4990.6040.0650.0690.7121.0000.6680.013-0.3520.182-0.3590.637
Credit to Agriculture0.0970.7630.8130.3110.3750.166-0.4160.4230.5230.6130.4940.6681.0000.231-0.2330.125-0.2710.455
FDI inflows to Agriculture0.1560.3590.225-0.089-0.0580.0430.1130.0020.3790.355-0.0800.0130.2311.000-0.004-0.2160.2000.081
Agriculture share of Government Expenditure-0.160-0.195-0.464-0.600-0.1860.0490.253-0.5620.1000.004-0.403-0.352-0.233-0.0041.000-0.3890.220-0.427
Water Use Efficiency0.066-0.0150.2710.5430.0780.135-0.1100.306-0.361-0.1640.4940.1820.125-0.216-0.3891.000-0.3190.250
Gini coefficient-0.0180.029-0.179-0.417-0.5610.2770.588-0.4060.2480.194-0.268-0.359-0.2710.2000.220-0.3191.000-0.314
Agri_Prod_Per_Capita0.3070.4600.5220.6060.3140.023-0.4010.6220.0110.0730.4620.6370.4550.081-0.4270.250-0.3141.000

Missing values

2023-12-13T13:15:37.165759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T13:15:37.370086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T13:15:37.550658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearTotal Agri ProductionGDP(1000 USD)GDP per capitaCountry_CodeNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)AreaPopulationFertilizer Use Per AreaFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
0Albania1993.01885819.01700439.125515.173587ALB6.2990.1812.1461.812231126.03300711.025.495.42NaN642888.02.930.260.270103571.337206
1Albania1994.01824978.01880950.821571.023495ALB3.9576.6913.2162.065851126.03293999.021.044.48NaN642888.02.930.260.270103554.031134
2Albania1995.01464974.02392764.887728.532187ALB5.08116.0311.8162.879731127.03284364.015.673.34NaN642888.02.930.260.270103446.044957
3Albania1996.01602148.03199642.024978.085686ALB4.80123.9611.9163.405571131.03271331.09.251.99NaN642888.02.930.260.270103489.754170
4Albania1997.01431983.02224654.241683.726604ALB5.3881.0012.1265.178711135.03253719.07.431.60NaN642888.02.930.260.277984440.106537
5Albania1998.01527417.02554868.837790.448796ALB5.48103.4712.4166.261951139.03232175.035.767.73NaN642888.02.930.260.285866472.566306
6Albania1999.01507759.03221670.1651004.179887ALB4.88107.2712.7966.394991145.03208260.015.163.30NaN642888.02.930.260.293747469.961599
7Albania2000.01548958.03487586.3021096.028688ALB5.5881.5612.5665.964611144.03182021.026.755.87NaN642888.02.930.260.301628486.784342
8Albania2001.01574063.03926887.5971245.203150ALB4.8596.1912.5466.365851139.03153612.026.765.93NaN642888.02.930.260.309509499.130204
9Albania2002.01658484.04355865.8891394.523697ALB3.91104.9112.6065.965811140.03123551.080.6918.06NaN642888.03.720.370.317390530.961076
CountryYearTotal Agri ProductionGDP(1000 USD)GDP per capitaCountry_CodeNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)AreaPopulationFertilizer Use Per AreaFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
1937Uruguay2007.05375504.023410566.867033.049382URY0.41113.5017.1388.4920714550.03328651.0122.8261.8620068104.0335020000.01.150.170.4640511614.919678
1938Uruguay2008.07357183.030366193.339102.232149URY0.0768.7917.7986.5878214674.03336126.092.3057.1422974252.0600982000.01.200.180.4508182205.307294
1939Uruguay2010.08286098.040284556.6512015.732222URY0.30103.4617.3790.5187814433.03352651.0128.8088.4032190854.0314364000.01.010.160.4447502471.506280
1940Uruguay2013.010951811.059963430.3017734.468529URY0.1790.4717.2595.1133614346.73381180.0223.25172.1658885727.0342442549.00.940.230.4045093239.049977
1941Uruguay2014.011280189.060484771.7817833.372482URY0.08133.9917.8195.6849414475.13391662.0163.43132.5263197725.043211760.00.990.220.4011113325.858827
1942Uruguay2015.08833548.057080759.0916774.555410URY0.20103.8217.8895.6132614467.63402818.0102.5282.6148841471.042023232.00.910.210.4012882595.950768
1943Uruguay2016.08067775.057236652.4916766.425259URY0.05111.8817.2597.3879814265.33413766.0135.5099.9045199938.0182055671.00.940.210.3969552363.306389
1944Uruguay2017.08943169.064233966.8618769.787523URY0.08113.8418.4298.6681314222.93422200.0138.98100.3245811604.0-88928363.00.880.170.3946452613.280638
1945Zimbabwe2012.01824286.017114849.881290.193956ZWE0.0150.6521.8066.5138616200.013265331.017.815.50NaNNaN5.670.04NaN137.522841
1946Zimbabwe2013.01197274.019091019.991408.367810ZWE0.0066.4221.5966.2462116200.013555422.025.007.56NaNNaN5.670.04NaN88.324362